Articles | Volume 18, issue 3
https://doi.org/10.5194/os-18-881-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-18-881-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Jun 2022
Research article |
| 09 Jun 2022
| 09 Jun 2022
Data-assimilation-based parameter estimation of bathymetry and bottom friction coefficient to improve coastal accuracy in a global tide model
Delft Institute of Applied Mathematics, Delft University of Technology, Delft, the Netherlands
Martin Verlaan
Delft Institute of Applied Mathematics, Delft University of Technology, Delft, the Netherlands
Deltares, Delft, the Netherlands
Jelmer Veenstra
Deltares, Delft, the Netherlands
Hai Xiang Lin
Delft Institute of Applied Mathematics, Delft University of Technology, Delft, the Netherlands
Related authors
Jianbing Jin, Arjo Segers, Hai Xiang Lin, Bas Henzing, Xiaohui Wang, Arnold Heemink, and Hong Liao
Geosci. Model Dev., 14, 5607–5622, https://doi.org/10.5194/gmd-14-5607-2021, https://doi.org/10.5194/gmd-14-5607-2021, 2021
Short summary
Short summary
When discussing the accuracy of a dust forecast, the shape and position of the plume as well as the intensity are key elements. The position forecast determines which locations will be affected, while the intensity only describes the actual dust level. A dust forecast with position misfit directly results in incorrect timing profiles of dust loads. In this paper, an image-morphing-based data assimilation is designed for realigning a simulated dust plume to correct for the position error.
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-216, https://doi.org/10.5194/gmd-2023-216, 2024
Preprint under review for GMD
Short summary
Short summary
The model evaluation against ground observations is usually unfair. The former simulates mean status over coarse grids while the latter represents the very surrounding atmosphere. To solve this, we proposed a new approach called "LUBR" that considers the intra-grid variance. The LUBR is validated to provide insights that align with satellite OMI measurements. The results highlight the importance of considering fine-scale urban-rural differences when comparing models and observation.
Jarno Verkaik, Edwin H. Sutanudjaja, Gualbert H. P. Oude Essink, Hai Xiang Lin, and Marc F. P. Bierkens
Geosci. Model Dev., 17, 275–300, https://doi.org/10.5194/gmd-17-275-2024, https://doi.org/10.5194/gmd-17-275-2024, 2024
Short summary
Short summary
This paper presents the parallel PCR-GLOBWB global-scale groundwater model at 30 arcsec resolution (~1 km at the Equator). Named GLOBGM v1.0, this model is a follow-up of the 5 arcmin (~10 km) model, aiming for a higher-resolution simulation of worldwide fresh groundwater reserves under climate change and excessive pumping. For a long transient simulation using a parallel prototype of MODFLOW 6, we show that our implementation is efficient for a relatively low number of processor cores.
Li Fang, Jianbing Jin, Arjo Segers, Hong Liao, Ke Li, Bufan Xu, Wei Han, Mijie Pang, and Hai Xiang Lin
Geosci. Model Dev., 16, 4867–4882, https://doi.org/10.5194/gmd-16-4867-2023, https://doi.org/10.5194/gmd-16-4867-2023, 2023
Short summary
Short summary
Machine learning models have gained great popularity in air quality prediction. However, they are only available at air quality monitoring stations. In contrast, chemical transport models (CTM) provide predictions that are continuous in the 3D field. Owing to complex error sources, they are typically biased. In this study, we proposed a gridded prediction with high accuracy by fusing predictions from our regional feature selection machine learning prediction (RFSML v1.0) and a CTM prediction.
Li Fang, Jianbing Jin, Arjo Segers, Hai Xiang Lin, Mijie Pang, Cong Xiao, Tuo Deng, and Hong Liao
Geosci. Model Dev., 15, 7791–7807, https://doi.org/10.5194/gmd-15-7791-2022, https://doi.org/10.5194/gmd-15-7791-2022, 2022
Short summary
Short summary
This study proposes a regional feature selection-based machine learning system to predict short-term air quality in China. The system has a tool that can figure out the importance of input data for better prediction. It provides large-scale air quality prediction that exhibits improved interpretability, fewer training costs, and higher accuracy compared with a standard machine learning system. It can act as an early warning for citizens and reduce exposure to PM2.5 and other air pollutants.
Jianbing Jin, Mijie Pang, Arjo Segers, Wei Han, Li Fang, Baojie Li, Haochuan Feng, Hai Xiang Lin, and Hong Liao
Atmos. Chem. Phys., 22, 6393–6410, https://doi.org/10.5194/acp-22-6393-2022, https://doi.org/10.5194/acp-22-6393-2022, 2022
Short summary
Short summary
Super dust storms reappeared in East Asia last spring after being absent for one and a half decades. Accurate simulation of such super sandstorms is valuable, but challenging due to imperfect emissions. In this study, the emissions of these dust storms are estimated by assimilating multiple observations. The results reveal that emissions originated from both China and Mongolia. However, for northern China, long-distance transport from Mongolia contributes much more dust than Chinese deserts.
Jianbing Jin, Arjo Segers, Hai Xiang Lin, Bas Henzing, Xiaohui Wang, Arnold Heemink, and Hong Liao
Geosci. Model Dev., 14, 5607–5622, https://doi.org/10.5194/gmd-14-5607-2021, https://doi.org/10.5194/gmd-14-5607-2021, 2021
Short summary
Short summary
When discussing the accuracy of a dust forecast, the shape and position of the plume as well as the intensity are key elements. The position forecast determines which locations will be affected, while the intensity only describes the actual dust level. A dust forecast with position misfit directly results in incorrect timing profiles of dust loads. In this paper, an image-morphing-based data assimilation is designed for realigning a simulated dust plume to correct for the position error.
Jianbing Jin, Arjo Segers, Hong Liao, Arnold Heemink, Richard Kranenburg, and Hai Xiang Lin
Atmos. Chem. Phys., 20, 15207–15225, https://doi.org/10.5194/acp-20-15207-2020, https://doi.org/10.5194/acp-20-15207-2020, 2020
Short summary
Short summary
Data assimilation provides a powerful tool to estimate emission inventories by feeding observations. This emission inversion relies on the correct assumption about the emission uncertainty, which describes the potential spatiotemporal spreads of sources. However, an unrepresentative uncertainty is unavoidable. Especially in the complex dust emission, the uncertainties can hardly all be taken into account. This study reports how adjoint can be used to detect errors in the emission uncertainty.
Jianbing Jin, Hai Xiang Lin, Arjo Segers, Yu Xie, and Arnold Heemink
Atmos. Chem. Phys., 19, 10009–10026, https://doi.org/10.5194/acp-19-10009-2019, https://doi.org/10.5194/acp-19-10009-2019, 2019
Guangliang Fu, Hai Xiang Lin, Arnold Heemink, Sha Lu, Arjo Segers, Nils van Velzen, Tongchao Lu, and Shiming Xu
Geosci. Model Dev., 10, 1751–1766, https://doi.org/10.5194/gmd-10-1751-2017, https://doi.org/10.5194/gmd-10-1751-2017, 2017
Short summary
Short summary
We propose a mask-state algorithm (MS) which records the sparsity information of the full ensemble state matrix and transforms the full matrix into a relatively small one. It will reduce the computational cost in the analysis step for plume assimilation applications. Ensemble-based DA with the mask-state algorithm is generic and flexible, because it implements exactly the standard DA without any approximation and it realizes the satisfying performance without any change of the full model.
Maurizio Mazzoleni, Martin Verlaan, Leonardo Alfonso, Martina Monego, Daniele Norbiato, Miche Ferri, and Dimitri P. Solomatine
Hydrol. Earth Syst. Sci., 21, 839–861, https://doi.org/10.5194/hess-21-839-2017, https://doi.org/10.5194/hess-21-839-2017, 2017
Short summary
Short summary
This study assesses the potential use of crowdsourced data in hydrological modeling, which are characterized by irregular availability and variable accuracy. We show that even data with these characteristics can improve flood prediction if properly integrated into hydrological models. This study provides technological support to citizen observatories of water, in which citizens can play an active role in capturing information, leading to improved model forecasts and better flood management.
Guangliang Fu, Fred Prata, Hai Xiang Lin, Arnold Heemink, Arjo Segers, and Sha Lu
Atmos. Chem. Phys., 17, 1187–1205, https://doi.org/10.5194/acp-17-1187-2017, https://doi.org/10.5194/acp-17-1187-2017, 2017
Short summary
Short summary
A Satellite Observational Operator (SOO) is proposed to translates satellite-retrieved 2-D volcanic ash mass loadings to 3-D concentrations. The SOO makes the analysis step of assimilation comparable in the 3-D model space, and thus it avoids the artificial vertical correlations by not involving the integral operator in directly assimilating 2-D data. The results show that satellite data assimilation with SOO can efficiently improve the estimate of volcanic ash state and the forecast.
Guangliang Fu, Arnold Heemink, Sha Lu, Arjo Segers, Konradin Weber, and Hai-Xiang Lin
Atmos. Chem. Phys., 16, 9189–9200, https://doi.org/10.5194/acp-16-9189-2016, https://doi.org/10.5194/acp-16-9189-2016, 2016
Short summary
Short summary
Assimilating aircraft in situ measurements can significantly improve aviation advice on distal part of volcanic ash plume.
Cited articles
Arbic, B. K., Mitrovica, J. X., MacAyeal, D. R., and Milne, G. A.: On the
factors behind large Labrador Sea tides during the last glacial cycle and the
potential implications for Heinrich events, Paleoceanography, 23, PA3211,
https://doi.org/10.1029/2007PA001573, 2008. a
Arbic, B. K., Wallcraft, A. J., and Metzger, E. J.: Concurrent simulation of
the eddying general circulation and tides in a global ocean model, Ocean
Model., 32, 175–187, https://doi.org/10.1016/j.ocemod.2010.01.007, 2010. a
AVISO: Global Tide – FES2014, AVISO [data set], https://www.aviso.altimetry.fr/en/data/products/auxiliary-products/global-tide-fes/description-fes2014.html, last access: 31 May 2022. a
Bij de Vaate, I., Vasulkar, A. N., Slobbe, D. C., and Verlaan, M.: The
Influence of Arctic Landfast Ice on Seasonal Modulation of the M2 Tide,
J. Geophys. Res.-Ocean., 126, e2020JC016630,
https://doi.org/10.1029/2020JC016630, 2021. a, b
Blakely, C. P., Ling, G., Pringle, W. J., Contreras, M. T., Wirasaet, D., Westerink, J. J.,
Moghimi, S., Seroka, G., Shi, L., Myers, E., and Owensby, M.: Dissipation and Bathymetric Sensitivities in an Unstructured Mesh
Global Tidal Model, Earth Space Sci. Open Arch., 127, e2021JC018178,
https://doi.org/10.1002/essoar.10509993.1, 2022. a
Cai, H., Toffolon, M., Savenije, H. H. G., Yang, Q., and Garel, E.: Frictional interactions between tidal constituents in tide-dominated estuaries, Ocean Sci., 14, 769–782, https://doi.org/10.5194/os-14-769-2018, 2018. a
Caldwell, P. C., Merrifield, M. A., and Thompson, P. R.: Sea level measured by
tide gauges from global oceans as part of the Joint Archive for Sea Level
(JASL) since 1846, NOAA National Centers for Environmental Information, NOAA National Centers for Environmental
Information
[data set], https://doi.org/10.7289/v5v40s7w, 2010. a
Caldwell, P. C., Merrifield, M. A., and Thompson, P. R.: Sea level measured by tide
gauges from global oceans — the Joint Archive for Sea Level holdings (NCEI
Accession 0019568), Version 5.5, NOAA National Centers for Environmental
Information [data set], https://doi.org/10.7289/V5V40S7W, 2015. a
Carrere, L., Lyard, F., Cancet, M., Guillot, A., and Roblou, L.: FES
2012: A New Global Tidal Model Taking Advantage of Nearly 20 Years of
Altimetry, in: 20 Years of Progress in Radar Altimatry, edited by:
Ouwehand, L., ESA Special Publication, 710, p. 13,
https://ui.adsabs.harvard.edu/abs/2013ESASP.710E..13C (last access: 31 May 2022), 2013. a
Cheng, Y. and Andersen, O. B.: Towards further improving DTU global ocean tide model in shallow waters and Polar Seas, OSTST, Poster in: Proceedings of the Ocean Surface Topography Science Team (OSTST) Meeting, Miami, FL, USA, 23–27
October, 2017. a
Chu, D., Zhang, J., Wu, Y., Jiao, X., and Qian, S.: Sensitivities of modelling
storm surge to bottom friction, wind drag coefficient, and meteorological
product in the East China Sea, Estuarine, Coast. Shelf Sci., 231,
106460, https://doi.org/10.1016/j.ecss.2019.106460, 2019. a
Colebrook, C. F., White, C. M., and Taylor, G. I.: Experiments with fluid
friction in roughened pipes, P. R. Soc. Lond.
A Mat., 161, 367–381,
https://doi.org/10.1098/rspa.1937.0150, 1937. a
Copernicus Marine In Situ Tac Data Management Team: Product User Manual
for multiparameter Copernicus In Situ TAC (PUM), Copernicus [data set], https://doi.org/10.13155/43494, 2021. a
Edwards, C. A., Moore, A. M., Hoteit, I., and Cornuelle, B. D.: Regional Ocean
Data Assimilation, Ann. Rev. Mar. Sci., 7, 21–42,
https://doi.org/10.1146/annurev-marine-010814-015821, 2015. a
Egbert, G. D. and Erofeeva, S. Y.: Efficient Inverse Modeling of Barotropic
Ocean Tides, J. Atmos. Ocean. Technol., 19, 183–204,
https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2, 2002. a
EMODnet: Data products, EMODnet [data set], https://www.emodnet-bathymetry.eu/data-products, last access: 31 May 2022. a
Hallegatte, S., Green, C., Nicholls, R., and Corfee-Morlot, J.: Future flood
losses in major coastal cities, Nat. Clim. Change, 802–806,
https://doi.org/10.1038/nclimate1979, 2013. a
Hart-Davis, M. G., Piccioni, G., Dettmering, D., Schwatke, C., Passaro, M., and Seitz, F.: EOT20: a global ocean tide model from multi-mission satellite altimetry, Earth Syst. Sci. Data, 13, 3869–3884, https://doi.org/10.5194/essd-13-3869-2021, 2021. a
Heemink, A., Mouthaan, E., Roest, M., Vollebregt, E., Robaczewska, K., and
Verlaan, M.: Inverse 3D shallow water flow modelling of the continental
shelf, Cont. Shelf Res., 22, 465–484,
https://doi.org/10.1016/S0278-4343(01)00071-1, 2002. a, b
Kagan, B. and Sofina, E.: Ice-induced seasonal variability of tidal constants
in the Arctic Ocean, Cont. Shelf Res., 30, 643–647,
https://doi.org/doi.org/10.1016/j.csr.2009.05.010, 2010. a
Kowalik, Z. and Proshutinsky, A. Y.: The Arctic ocean tides, Washington DC American
Geophysical Union Geophysical Monograph Series, 85, 137–158,
https://doi.org/10.1029/GM085p0137, 1994. a, b
Kron, W.: Coasts: the high-risk areas of the world, Nat. Hazards, 66,
1363–1382, https://doi.org/10.1007/s11069-012-0215-4, 2012. a
Kuhlmann, J., Dobslaw, H., and Thomas, M.: Improved modeling of sea level
patterns by incorporating self-attraction and loading, J. Geophys.
Res.-Ocean., 116, C11036, https://doi.org/10.1029/2011JC007399, 2011. a
Lyard, F. H., Allain, D. J., Cancet, M., Carrère, L., and Picot, N.: FES2014
global ocean tide atlas: design and performance, Ocean Sci., 17, 615–649,
https://doi.org/10.5194/os-17-615-2021, 2021. a, b, c
Mayo, T., Butler, T., Dson, C. N., and Hoteit, I.: Data assimilation within the
Advanced Circulation (ADCIRC) modeling framework for the estimation of
Manning's friction coefficient, Ocean Model., 76, 43–58,
https://doi.org/10.1016/j.ocemod.2014.01.001, 2014. a, b
McGranahan, G., Balk, D., and Anderson, B.: The rising tide: assessing the
risks of climate change and human settlements in low elevation coastal zones,
Environ. Urban., 19, 17–37, https://doi.org/10.1177/0956247807076960,
2007. a
Muis, S., Verlaan, M., Nicholls, R. J., Brown, S., Hinkel, J., Lincke, D.,
Vafeidis, A. T., Scussolini, P., Winsemius, H. C., and Ward, P. J.: A
comparison of two global datasets of extreme sea levels and resulting flood
exposure, Earth's Future, 5, 379–392, https://doi.org/10.1002/2016EF000430, 2017. a
Muis, S., Apecechea, M. I., Dullaart, J., de Lima Rego, J., Madsen, K. S., Su,
J., Yan, K., and Verlaan, M.: A High-Resolution Global Dataset of Extreme Sea
Levels, Tides, and Storm Surges, Including Future Projections, Front.
Mar. Sci., 7, 263, https://doi.org/10.3389/fmars.2020.00263, 2020. a
Munk, W. and Wunsch, C.: Abyssal recipes II: energetics of tidal and wind
mixing, Deep-Sea Res. Pt. I, 45,
1977–2010, https://doi.org/10.1016/S0967-0637(98)00070-3, 1998. a
Müller, M., Cherniawsky, J. Y., Foreman, M. G. G., and von Storch, J.-S.:
Global M2 internal tide and its seasonal variability from high resolution
ocean circulation and tide modeling, Geophys. Res. Lett., 39, L19607,
https://doi.org/10.1029/2012GL053320, 2012. a
Müller, M., Cherniawsky, J. Y., Foreman, M. G. G., and von Storch, J.-S.:
Seasonal variation of the M2 tide, Ocean Dynam., 64, 159–177,
https://doi.org/10.1007/s10236-013-0679-0, 2014. a
Navon, I.: Practical and theoretical aspects of adjoint parameter estimation
and identifiability in meteorology and oceanography, Dynam. Atmos.
Ocean., 27, 55–79, https://doi.org/10.1016/S0377-0265(97)00032-8, 1998. a
Nycander, J.: Generation of internal waves in the deep ocean by tides, J. Geophys. Res.-Ocean., 110, C10028, https://doi.org/10.1029/2004JC002487, 2005. a, b
OpenDA Association: OpenDA, GitHub [code], https://github.com/OpenDA-Association/OpenDA, last access: 31 May 2022. a
OpenDA User Documentation: https://www.openda.org/docu/openda_2.4/doc/OpenDA_documentation.pdf,
last access: 10 June 2016. a
Open Source Community: Delft3D FM, https://oss.deltares.nl/web/delft3dfm, last access: 31 May 2022. a
Oppenheimer, M., Glavovic, B., Hinkel, J., Wal, R. V. D., Magnan, A.,
Abd-EIGawad, A., Cai, R., Cifuentes-Jara, M., DeConto, R., Ghosh, T., Hay,
J., Isla, F., Marzeion, B., Meyssignac, B., and Sebesvári, Z.: Sea Level Rise and Implications for
Low Lying Islands, Coasts and Communities, in: IPCC Special Report on the Ocean
and Cryosphere in Climate, Cambridge University Press, Cambridge, 321–446
https://doi.org/10.1017/9781009157964.006, 2019. a
Pringle, W. J., Wirasaet, D., Roberts, K. J., and Westerink, J. J.: Global storm tide modeling with ADCIRC v55: unstructured mesh design and performance, Geosci. Model Dev., 14, 1125–1145, https://doi.org/10.5194/gmd-14-1125-2021, 2021. a
Provost, C. and Lyard, F.: Energetics of the M2 barotropic ocean tides: an
estimate of bottom friction dissipation from a hydrodynamic model, Prog. Oceanogr., 40, 37–52, https://doi.org/10.1016/S0079-6611(97)00022-0, 1997. a
Pugh, D. and Woodworth, P.: Sea-Level Science: Understanding Tides, Surges,
Tsunamis and Mean Sea-Level Changes, Cambridge University Press,
https://doi.org/10.1017/CBO9781139235778, 2014. a
Ralston, M. L. and Jennrich, R. I.: Dud, A Derivative-Free Algorithm for
Nonlinear Least Squares, Technometrics, 20, 7–14,
https://doi.org/10.1080/00401706.1978.10489610, 1978. a
Ray, R. D.: Precise comparisons of bottom-pressure and altimetric ocean tides,
J. Geophys. Res.-Ocean, 118, 4570–4584,
https://doi.org/10.1002/jgrc.20336, 2013. a, b, c
Schureman, P.: Manual of Harmonic Analysis and Prediction of Tides, US Department
of Commerce, Coast and Geodetic Survey, https://doi.org/10.25607/OBP-155, 1958. a
Siripatana, A., Mayo, T., Knio, O., Dawson, C., Maître, O. L., and Hoteit, I.:
Ensemble Kalman filter inference of spatially-varying Manning's n
coefficients in the coastal ocean, J. Hydrol., 562, 664–684,
https://doi.org/10.1016/j.jhydrol.2018.05.021, 2018. a
Slivinski, L., Pratt, L., Rypina, I., Orescanin, M., Raubenheimer, B.,
MacMahan, J., and Elgar, S.: Assimilating Lagrangian data for parameter
estimation in a multiple-inlet system, Ocean Model., 113, 131–144,
https://doi.org/10.1016/j.ocemod.2017.04.001, 2017. a
Stammer, D., Ray, R. D., Andersen, O. B., Arbic, B. K., Bosch, W., Carrère,
L., Cheng, Y., Chinn, D. S., Dushaw, B. D., Egbert, G. D., Erofeeva, S. Y.,
Fok, H. S., Green, J. A. M., Griffiths, S., King, M. A., Lapin, V., Lemoine,
F. G., Luthcke, S. B., Lyard, F., Morison, J., Müller, M., Padman, L.,
Richman, J. G., Shriver, J. F., Shum, C. K., Taguchi, E., and Yi, Y.:
Accuracy assessment of global barotropic ocean tide models, Rev.
Geophys., 52, 243–282, https://doi.org/10.1002/2014RG000450, 2014. a, b, c, d, e, f, g, h, i, j
Taguchi, E., Stammer, D., and Zahel, W.: Inferring deep ocean tidal energy
dissipation from the global high-resolution data-assimilative HAMTIDE
model, J. Geophys. Res.-Ocean., 119, 4573–4592,
https://doi.org/10.1002/2013JC009766, 2013. a
Tozer, B., Sandwell, D. T., Smith, W. H. F., Olson, C., Beale, J. R., and
Wessel, P.: Global Bathymetry and Topography at 15 Arc Sec: SRTM15+, Earth
Space Sci., 6, 1847–1864, https://doi.org/10.1029/2019EA000658, 2019. a
Trémolet, Y.: Incremental 4D-Var convergence study, Tellus A, 59,
706–718, https://doi.org/10.1111/j.1600-0870.2007.00271.x, 2007. a
Ullman, D. S. and Wilson, R. E.: Model parameter estimation from data
assimilation modeling: Temporal and spatial variability of the bottom drag
coefficient, J. Geophys. Res.-Ocean., 103, 5531–5549,
https://doi.org/10.1029/97JC03178, 1998. a
Verlaan, M., De Kleermaeker, S., and Buckman, L.: GLOSSIS: Global storm surge
forecasting and information system, Auckland, New Zealand,
Engineers Australia and IPENZ, 229–234,
https://doi.org/10.3316/informit.703696922952912,
2015. a, b
Vitousek, S., Barnard, P., Fletcher, C., Frazer, N., Erikson, L., and
Storlazzi, C.: Doubling of coastal flooding frequency within decades due to
sea-level rise, Sci. Rep., 7, 1–9, https://doi.org/10.1038/s41598-017-01362-7,
2017. a
Wahl, T., Haigh, I. D., Nicholls, R. J., Arns, A., Dangendorf, S.,
Hinkel, J., and Slangen, A. B. A.: Understanding extreme sea levels for
broad-scale coastal impact and adaptation analysis, Nat. Commun.,
8, 16075, https://doi.org/10.1038/ncomms16075, 2017. a
Wang, D., Zhang, J., and Wang, Y. P.: Estimation of Bottom Friction Coefficient
in Multi-Constituent Tidal Models Using the Adjoint Method: Temporal
Variations and Spatial Distributions, J. Geophys. Res.-Ocean., 126, e2020JC016949, https://doi.org/10.1029/2020JC016949, 2021a. a
Wang, X., Verlaan, M., Apecechea, M. I., and Lin, H. X.: Parameter estimation
for a global tide and surge model with a memory-efficient order reduction
approach, Ocean Model., 173, 102011, https://doi.org/10.1016/j.ocemod.2022.102011,
2022. a, b
Ward, P., Jongman, B., Salamon, P., Simpson, A., Bates, P., de Groeve, T.,
Muis, S., Coughlan, E., Rudari, R., Trigg, M., and Winsemius, H.: Usefulness
and limitations of global flood risk models, Nat. Clim. Change, 5,
712–715, https://doi.org/10.1038/nclimate2742, 2015. a
Wölfl, A.-C., Snaith, H., Amirebrahimi, S., Devey, C. W., Dorschel, B.,
Ferrini, V., Huvenne, V. A. I., Jakobsson, M., Jencks, J., Johnston, G.,
Lamarche, G., Mayer, L., Millar, D., Pedersen, T. H., Picard, K., Reitz, A.,
Schmitt, T., Visbeck, M., Weatherall, P., and Wigley, R.: Seafloor Mapping –
The Challenge of a Truly Global Ocean Bathymetry, Front. Mar.
Sci., 6, 283, https://doi.org/10.3389/fmars.2019.00283, 2019. a
Zaron, E. D.: Topographic and frictional controls on tides in the Sea of
Okhotsk, Ocean Model., 117, 1–11, https://doi.org/10.1016/j.ocemod.2017.06.011,
2017.
a
Zhang, S., Liu, Z., Zhang, X., Wu, X., Han, G., Zhao, Y., Yu, X., Liu, C., Liu,
Y., Wu, S., Lu, F., Li, M., and Deng, X.: Coupled data assimilation and
parameter estimation in coupled ocean–atmosphere models: a review, Clim.
Dynam., 54, 5127–5144, https://doi.org/10.1007/s00382-020-05275-6, 2020. a
Zijl, F., Verlaan, M., and Gerritsen, H.: Improved water-level forecasting for
the Northwest European Shelf and North Sea through direct modelling of tide,
surge and non-linear interaction, Ocean Dynam., 63, 823–847,
https://doi.org/10.1007/s10236-013-0624-2, 2013. a, b, c
Short summary
The accuracy of the Global Tide and Surge Model is significantly affected by some parameters. We correct the bathymetry and bottom friction coefficient with mathematical methods to improve model accuracy. The lack of tide gauge data in many coastal areas affects the correction process. We propose using observations from altimetry tidal products like FES2014 that have higher accuracy than our model to offset the data lack. Model accuracy is greatly improved, especially in the European shelf.
The accuracy of the Global Tide and Surge Model is significantly affected by some parameters. We...
